Advanced Proteomics Techniques and Applications

ProteoPlotter is a visualization tool for proteomics data that complements Perseus, offering enhanced features for data analysis and interpretation.

Pfly predicts peptide detectability in mass spectrometry using a deep learning model trained on synthetic and biological data, offering customizable classification for different experimental conditions.

This study evaluates the use of FAIMS with Orbitrap Ascend Tribrid Mass Spectrometer to enhance AP-MS analysis of SAP25 protein complexes, showing improved sensitivity and reduced contaminants.

The paper demonstrates using data-independent acquisition (DIA) to select peptides for targeted proteomics assays, achieving comparable quantitative precision to existing methods.

The paper identifies a signature for protein leakage in single-cell proteomics and develops a classifier to detect damaged cells across species and cell types.

This study evaluates the Orbitrap Astral MS's sensitivity for single-cell proteomics, showing improved proteome coverage and quantitative accuracy in low-input samples.

This paper introduces a simple one-step vial-based pretreatment method for deep single-cell proteomics and applies it to study oocyte aging, identifying age-related protein markers.

This study uses reactivity proteomics to analyze ATP-induced changes in Arabidopsis proteomes, revealing conformational shifts and metabolite-protein interactions.

The paper introduces limpa, a package for quantifying and analyzing MS proteomics data, handling missing values probabilistically to improve differential expression analysis.

The paper audits error control in twelve crosslinking mass spectrometry pipelines using ground-truth designs, finding some pipelines exceed false discovery rates by up to 29-fold.

The paper introduces SPARCE, a method for multiplexed phosphoproteomic analysis of low cell numbers, demonstrating its effectiveness in quantifying phosphosite changes from 1,000 FACS-sorted cells.

CHIMERYS deconvolves chimeric spectra in proteomic data, enabling unified analysis across DDA, DIA, and PRM experiments by accurately identifying and quantifying multiple peptides per spectrum.

The paper develops a workflow for targeted proteomics using a hybrid quadrupole-linear ion trap instrument, demonstrating consistent quantification without high-mass accuracy.

This paper introduces an in-cell proteomics method for C. elegans that enables high-throughput proteome analysis with minimal sample input and identifies proteomic changes in sod-1 mutants.

ibaqpy computes iBAQ values for proteomics data using SDRF metadata, improving scalability and reproducibility in large-scale experiments.

The paper evaluates normalization methods in proteomics data, finding that some methods perform better than others and develops a tool to assess their impact.

The paper introduces Live Eco-AI, a cost-effective single-cell mass spectrometry proteomics method using CE and AI, achieving performance comparable to advanced instruments.

This study uses spatial proteomics and machine learning to analyze AATD in human liver tissue, revealing peroxisomal upregulation and a terminal hepatocyte state with distinct proteomic signatures.

The paper introduces mspms, an R package and graphical interface for analyzing MSP-MS data, validated using cathepsin substrate profiling.

DIA-BERT improves DIA proteomics analysis by using pre-trained transformer models, achieving higher protein and peptide identifications than DIA-NN in cancer samples.

The paper evaluates proteome analysis performance across different chromatographic flow rates and shows that all are suitable for high-quality analysis, with capLC being a robust alternative to nanoLC.

This study compares DIA-MS and DDA-MS for metaproteomics, finding DIA-MS provides more reproducible and accurate protein and peptide identifications across laboratories.

This study creates a high-resolution morpho-proteomic atlas of mitosis by decomposing it into 40 subsections and quantifying 4,350 proteins, revealing dynamic changes and links to cancer mutations.

This study assesses the impact of a single freeze-thaw cycle on serum protein stability using mass spectrometry, finding significant abundance changes for 30 proteins, with 11 validated as potential biomarkers for pancreatic disease.

MSFragger-DDA+ improves peptide identification sensitivity by searching the full isolation window and handling co-fragmented peptides, enhancing proteomics data analysis.

The paper develops a proteomics strategy to infer the origin of proteins in human plasma using a proteome atlas and RNA/protein atlases, demonstrating its application in disease cohorts.

CellEKT profiles cellular target engagement of kinase inhibitors using chemical proteomics to determine IC50 values across over 300 kinases and validate results with phosphoproteomics and NanoBRET.

The paper introduces toma-scpMS, a method that improves quantification in multiplexed single cell proteomics by separating carrier and single cell proteomes during mass spectrometry.

This study introduces SC-pSILAC, a method to measure protein abundance and turnover in single cells, revealing differentiation-specific dynamics and cell-type markers in human iPSCs.

This study compares urinary proteome modifications between methamphetamine-abstinent rehabilitation patients and healthy individuals, identifying 984 differentially modified peptides and suggesting new insights into addiction rehabilitation.